docstr: This function scores the full dataset at once. See BatchContainer below for batched scoring.
docstr_tok: ["This","function","scores","the","full","dataset","at","once",".","See","BatchContainer","below","for","batched","scoring","."]
code: def _run(self, function, *inputs):
      """
      This function scores the full dataset at once. See BatchContainer below for batched scoring.
      """
      if DataFrame is not None and isinstance(inputs[0], DataFrame):
          if self._check_dataframe_to_array:
              # We were expecting a numpy array as input but we got a dataframe: call values() on it to get the array.
              inputs = [inputs[0].values]
          else:
              # Split the dataframe into column ndarrays.
              inputs = inputs[0]
              input_names = list(inputs.columns)
              splits = [
                  inputs[input_names[idx]]
                  for idx in range(len(input_names))
              ]
              inputs = [df.to_numpy().reshape(-1, 1) for df in splits]
  
      return function(*inputs)
  
imports: from abc import ABC, abstractmethod
  from hummingbird.ml._utils import pandas_installed
  from hummingbird.ml.operator_converters import constants
label_desc: Use image node_id 4 for calling the SklearnContainer obj's underlying member method code with example usage: obj._run(function) and returns: function